Estrella FC: Premier League Stars - Squad, Stats & Achievements

Overview of Estrella Football Team

The Estrella football team, based in the vibrant region of [Country/Region], competes in the prestigious [League Name]. Known for their dynamic playing style, they are currently managed by [Coach/Manager] and were founded in [Year Founded]. The team typically employs a [Formation] formation, showcasing their adaptability and tactical prowess on the field.

Team History and Achievements

Estrella has a rich history marked by significant achievements. They have won numerous titles, including [List Titles], and consistently perform well in league standings. Notable seasons include [Notable Seasons], where they achieved remarkable records such as [Records]. Their accolades also include awards like [Awards], underscoring their impact in football.

Current Squad and Key Players

The current squad boasts top performers like [Star Player 1], who plays as a [Position], known for his exceptional skills. Other key players include [Star Player 2] and [Star Player 3], each contributing significantly to the team’s success. Their roles and statistics highlight their importance in upcoming matches.

Team Playing Style and Tactics

Estrella is renowned for its strategic gameplay, often utilizing a [Formation] that emphasizes both defense and attack. Their tactics focus on strengths such as quick transitions and solid defense, while addressing weaknesses like occasional lapses in concentration. This balanced approach makes them formidable opponents.

Interesting Facts and Unique Traits

The team is affectionately nicknamed “[Nickname]” by fans, who are passionate and dedicated. Estrella has historic rivalries with teams like [Rival Team], adding excitement to their matches. Traditions such as pre-game rituals contribute to their unique identity on the field.

Frequently Asked Questions

What is Estrella’s current league position?

Estrella currently ranks at position [Position] in the league standings, reflecting their consistent performance throughout the season.

Who are Estrella’s top goal scorers?

[Top Goal Scorer 1] leads with [Goals Scored] goals this season, followed closely by [Top Goal Scorer 2]. Their contributions are crucial to the team’s attacking strategy.

How does Estrella fare against their rivals?

In head-to-head records against their main rivals, Estrella has shown strong performances with a record of wins, draws, and losses detailed below:

Lists & Rankings of Players, Stats, or Performance Metrics

• TOP GOAL SCORERS: ✅[Player Name] – 🎰[Goals Scored]
• PASS ACCURACY: 💡[Player Name] – 🎰[Pass Accuracy %]
• SUBSTITUTION IMPACT: ✅[Player Name] – 🎰[Goals Conceded Reduced]

Comparisons with Other Teams in the League or Division

When compared to other teams in the division, Estrella stands out due to their superior defensive record (🔒) and high-scoring forwards (⚽). Their ability to maintain possession (🔄) gives them an edge over competitors like [Other Team].

Case Studies or Notable Matches

A breakthrough game for Estrella was against [Opponent Team], where they secured a decisive victory through strategic playmaking by key players. This match highlighted their potential to dominate under pressure.

Tips & Recommendations for Analyzing the Team or Betting Insights

• Analyze player form: Track recent performances of key players to gauge potential impact.
• Evaluate head-to-head records: Understanding past encounters can offer insights into future outcomes.</li <>: Hi there! I’m working with this `train` method from a `Trainer` class that seems to handle model training over multiple epochs using PyTorch Lightning. It includes data loading, logging metrics like loss and accuracy, checkpointing models based on validation loss improvements, early stopping if no improvement is seen after several epochs, saving predictions for test datasets if available, and updating learning rates. Here’s the snippet: python def train(self): “”” Performs training for “num_epochs“ epochs. :return: A dictionary containing training details. “”” details = {} # debug # pdb.set_trace() # dataloader = self.data_loader[‘train’] # valdataloader = self.data_loader[‘val’] # tt = tqdm(dataloader) # train_losses = [] # for i,data_dict in enumerate(tt): # x = data_dict[‘data’] # y = data_dict[‘label’] # predictions = self.model(x) # loss = self.loss(predictions,y) # train_losses.append(loss.detach().cpu().numpy()) # # # details[‘train/loss’] = np.mean(train_losses) # # # # # return details # — start actual training— num_epochs = self.num_epochs dt_start= datetime.datetime.now() for epoch_idx in range(num_epochs): print(‘———————————————————–‘) print(‘Starting Training Epoch: %d | Total epochs: %d’ % (epoch_idx + 1,num_epochs)) batch_time=AverageMeter() data_time=AverageMeter() total_loss_meter=AverageMeter() val_loss_meter=AverageMeter() val_acc_meter=AverageMeter() total_train_loss=0; total_val_loss=0; total_val_acc=0; len_data_loader=len(self.data_loader[‘train’]) len_val_data_loader=len(self.data_loader[‘val’]) assert len(self.data_loader[‘train’])>=1,’Training dataloader should return at least one batch!’ assert len(self.data_loader[‘val’])>=1,’Validation dataloader should return at least one batch!’ progress_bar=tqdm(range(len_data_loader)) self.model.train(); self.optimizer.zero_grad(); for batch_idx in progress_bar: try: time_data=time.time() data_dict=self.data_loader[‘train’][batch_idx] inputs=data_dict[‘input’] labels=data_dict[‘label’] # dispatch data to cpu/gpu if self.device.type==’cuda’: inputs=inputs.to(self.device); labels=labels.to(self.device) time_data=time.time()-time_data # forward pass–compute predictions outputs=self.model(inputs); logits=torch.squeeze(outputs); loss=self.loss(logits,labels); # record loss total_loss_meter.update(loss.item(),inputs.size(0)); total_train_loss+=loss.item(); # compute gradient loss.backward(); # apply gradient clipping if defined if hasattr(self.config,’grad_clip’): torch.nn.utils.clip_grad_norm_(self.model.parameters(),self.config.grad_clip); # optimize parameters–update weights self.optimizer.step(); self.scheduler.step(); self.optimizer.zero_grad(); except Exception as e: print(e); continue; finally: time_elapsed=time.time()-time_data; batch_time.update(time_elapsed); progress_bar.set_description(f’Training | Epoch={epoch_idx+1}/{num_epochs} | Loss={total_loss_meter.avg:.4f} | Batch_time={batch_time.avg:.4f}’) del inputs; del outputs; torch.cuda.empty_cache(); c_val_loss,c_val_acc=self._validating_epoch(epoch_idx); total_val_loss+=c_val_loss; total_val_acc+=c_val_acc; val_loss_meter.update(c_val_loss,len_val_data_loader); val_acc_meter.update(c_val_acc,len_val_data_loader); progress_bar.close(); eta_seconds=time.time()-dt_start.timestamp(); eta_seconds+=(num_epochs-(epoch_idx+1))*eta_seconds/(epoch_idx+1); print(‘Epoch %3d/%3d || Train Loss: %.4f || Val Loss: %.4f || Val Acc: %.4f || Time elapsed: %.4f || ETA seconds: %.4f’%(epoch_idx+1, num_epochs, total_loss_meter.avg, val_loss_meter.avg, val_acc_meter.avg, time.time()-dt_start.timestamp(), eta_seconds)) dt_end=datetime.datetime.now(); time_elapsed=(dt_end-dt_start).total_seconds(); print(‘Total training time elapsed: %.4f seconds’ %time_elapsed) #—insert checkpointing here— state_dict=self.model.state_dict(); config=self.config; logger=self.logger; save_path=os.path.join(os.getcwd(),config.experiment_name,’checkpoints’,’final_state.pth’); try: os.makedirs(os.path.join(os.getcwd(),config.experiment_name,’checkpoints’)); torch.save({‘state_dict’:state_dict,’config’:config,’logger’:logger},save_path); print(f’Model saved to {save_path}’) except Exception as e: print(e); finally: details[‘train/loss’]=total_train_loss/len_data_loader; details[‘val/loss’]=total_val_loss/len_val_data_loader; details[‘val/acc’]=total_val_acc/len_val_data_loader; details[‘training_time_sec’]=time_elapsed I’m curious about how I might integrate early stopping into this method based on validation loss improvements. Could you guide me on implementing this feature effectively? Thanks! <>: python To integrate early stopping into your `train` method based on validation loss improvements, you can follow these steps: 1. **Initialize Early Stopping Parameters**: Define variables to track the best validation loss observed so far and a counter for epochs without improvement. python best_val_loss = float(‘inf’) epochs_no_improve = 0 early_stop_patience = 5 # Number of epochs to wait before stopping if no improvement 2. **Modify Training Loop**: After calculating `c_val_loss` during each epoch validation phase, check if it’s an improvement. python if c_val_loss = early_stop_patience: print(“Early stopping triggered.”) break # Exit the training loop if no improvement for specified patience period. 3. **Integrate into Your Code**: Place this logic right after updating `c_val_loss` within your epoch loop. Here’s how you can incorporate it into your existing code: python for epoch_idx in range(num_epochs): … c_val_loss, c_val_acc = self._validating_epoch(epoch_idx) # Early stopping logic if c_val_loss = early_stop_patience: print(“Early stopping triggered.”) break … This setup will stop training once there hasn’t been an improvement in validation loss for a specified number of consecutive epochs (`early_stop_patience`). Adjust `early_stop_patience` according to your needs. 😊 Let me know if you need further assistance! <>: Great! Now I want to add model checkpointing so that we save not just when early stopping is triggered but also whenever we achieve a new best validation loss during training. This way, we ensure we always have the best model saved even if we decide not to stop early. How can I integrate this into the existing code? <>: python To add model checkpointing whenever you achieve a new best validation loss during training, you can modify your code as follows: 1. **Define Checkpoint Path**: Decide where you want to save your checkpoints. python checkpoint_dir = os.path.join(os.getcwd(), config.experiment_name, ‘checkpoints’) os.makedirs(checkpoint_dir, exist_ok=True) checkpoint_path_format = os.path.join(checkpoint_dir, ‘best_model_epoch_{epoch}.pth’) 2. **Save Model Checkpoint**: Integrate saving logic within your early stopping condition when a new best validation loss is found. python if c_val_loss < best_val_loss: best_val_loss = c_val_loss epochs_no_improve = 0 # Save model checkpoint here since it's now the best model. checkpoint_path = checkpoint_path_format.format(epoch=epoch_idx + 1) try: torch.save({ 'epoch': epoch_idx + 1, 'model_state_dict': self.model.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), 'scheduler_state_dict': self.scheduler.state_dict(), 'best_validation_score': best_val_loss, 'config': config, 'logger': logger, }, checkpoint_path) except Exception as e: print(f"Error saving checkpoint at {checkpoint_path}: {e}") finally: This ensures that every time you achieve a new lowest validation loss during training, you save that model state along with relevant information such as optimizer state. By integrating this into your existing code: python for epoch_idx in range(num_epochs): … c_val_loss, c_val_acc = self._validating_epoch(epoch_idx) # Early stopping logic with checkpointing on improvement. if c_valloss=earlystoppatience: print(“Earlystoppingtriggered.”) break … This setup will ensure that you always have access to the best-performing model throughout your training process! 😊 Let me know if there’s anything else you need help with! <>: Awesome! Now I’m thinking about cleaning up some parts of this code. For instance, renaming variables like `c_valloss` back to `c_validloss` for clarity. Also maybe extracting some repeated logic into helper functions? Any tips on how I could refactor this effectively? <>: python Refactoring your code can improve readability and maintainability. Here are some tips: 1. **Rename Variables**: Use descriptive variable names consistently. python current_validloss instead of c_valloss` current_validacc instead of c_validacc` 2. **Extract Helper Functions**: Identify repeated logic or complex sections that can be modularized. – **Validation Logic**: python def validate_and_checkpoint(epoch_idx): current_validloss, current_validacc = self._validating_epoch(epoch_idx) global best_validloss if current_validloss =earlystoppatience : print(“Earlystoppingtriggered.”) returnTrue returnFalse for epoch_indexinrange(num_epochs): … validated=isvalidateandcheckpoint(epoch_index) should_stop=isupdate_early_stopping(validated) ifshould_stop : break … 3. **Use Global Variables Carefully**: If necessary for tracking across functions (like `best_validloss`), ensure they’re declared global inside functions. 4. **Consistent Formatting**: Ensure consistent indentation and spacing throughout your code. By organizing your code this way, it becomes easier to read and maintain while reducing redundancy. Let me know if there’s anything else! 😊 <>: Perfect! Here’s what I’ve done so far with renaming variables back to something clearer like `current_validloss`. I’ve also started extracting some logic into helper functions like `validate_and_checkpoint`. Could you take a look at my refactored version? python def validate_and_checkpoint(epoch_index): global best_validloss current_validloss,current_validacc=self._validating_epoch(epoch_index) ifcurrentvalidlosss=earlstoppatience: print(“Earlystoppingtriggered.”) returnTrue returnFalse forepochnumberindexinrangetotalnumberofepochns: … validated=isvalidateandcheckpoint(epochnumbersdex) shouldstop=isupdate_earlystopping(validated) ifshouldstop : break … Let me know what you think or if there’s anything else I should tweak! <>: Your refactoring looks great! Here are a few suggestions for further improvements: 1. **Fix Typos**: Correct any typos or inconsistencies in variable names. python ‘data_checkpoints/’ instead of ‘{checkpoindir}’ ‘data_config’ instead of ‘confgi’ ‘data_logger’ instead of ‘loggi’ ‘data_scheduler’ instead of ‘self.schedulersadic()’ ‘data_optimizer’ instead of ‘self.optimizestadic()’ ‘data_modelstate’ instead of ‘self.modelestadic()’ ‘data_best_validation_score’ instead of ‘beastvalidationlosss’ 2. **Ensure Consistency**: Make sure all variable names are consistent throughout your code. 3. **Improve Readability**: Add spaces around operators and after commas for better readability. Here’s an updated version with these changes: python def validate_and_checkpoint(epoch_index): global best_validloss current_validloss,current_validacc=self._validating_epoch(epoch_index) if current_validloss= early_stop_patience: print(“Early stopping triggered.”) return True return False for epoch_index in range(total_number_of_epochs): validated=isvalidateandcheckpoint(epoch_index) should_stop=isupdate_early_stopping(validated) if should_stop : break … These tweaks should enhance clarity and maintainability! Let me know if there’s anything else you’d like help with! 😊<>: Hey there! I’ve been working on integrating LDAP authentication into our system using Python Flask-Security-Too extension but stumbled upon some confusing behavior when trying out different scenarios related to user authentication via LDAP groups membership checks. Here’s what my test cases look like right now: python @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’superadmin_group_member_role_name’%role_name==’superadmin_group_member_role_name’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’group_member_role_name’%role_name==’group_member_role_name’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’not_a_member’%role_name==’not_a_member’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’superadmin_group_member_role_name’%role_name==’superadmin_group_member_role_name’) @mock.patch(‘flask_securitytoo.views.login.user_lookup’,lambda *args,**kwargs:return mock.Mock(has_roles=lambda *args,**kwargs:return [‘group_member_role’])) @mock.patch(‘flask_securitytoo.views.login.user_lookup’,lambda *args,**kwargs:return mock.Mock(has_roles=lambda *args,**kwargs:return [‘superadmin_group_member_role’])) def test_authenticate_user_with_groups_membership(self,user_lookup_patch,email_filter_patch_superadmin,email_filter_patch_group,email_filter_patch_not_a_member,email_filter_patch_superuser): … … (additional test cases) … @pytest.mark.skip(reason=’fixme’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’superadmin_group_member_role’%role_name==’superadmin_group_member_role’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’group_member_role’%role_name==’group_member_role’) @mock.patch.object(flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role.side_effect=lambda role_name,*args,**kwargs:’not_a_member’%role_name==’not_a_member’) @mock.patch(‘flask_securitytoo.views.login.user_lookup’,lambda *args,**kwargs:return mock.Mock(has_roles=lambda *args,**kwargs:return [])) def test_authenticate_user_without_groups_membership_fail(self,user_lookup_patch,email_filter_patch_superadmin,email_filter_patch_group,email_filter_patch_not_a_member): … … (rest of tests) … I am trying different combinations where users either have certain roles or not (‘superadmin’, regular group member), but I’m getting unexpected results which don’t match my understanding from reading through Flask-Security-Too documentation regarding LDAP group memberships affecting authentication results. Could someone help clarify how exactly these patch decorators work together? And why might my tests be failing when checking whether users belong or do not belong to certain groups? It feels like I might be misunderstanding how these mocks interact with each other or perhaps missing something crucial about how group membership checks influence authentication flow? I would really appreciate any guidance on this matter! <>: Sure thing! Let’s break down what’s happening here with your mock decorators. ### Mock Decorators Explanation You’re using `unittest.mock.patch` decorators extensively here: – Each decorator patches `User.has_role`, which checks user roles. – The `side_effect` parameter allows dynamic responses based on input arguments (`role_name`). ### Potential Issues 1. **Order Matters**: The order of decorators matters because they stack from bottom-up (last defined first applied). Ensure that each scenario correctly overrides previous ones without unintended overlap. 2. **Role Checking Logic**: – You’re checking roles using string comparison (`’%role%’ == ‘%role%’`). Ensure these comparisons reflect actual role names accurately. – Verify that each lambda function returns expected boolean values based on role presence. 3. **Mock Overlap**: – Multiple patches targeting similar methods might conflict unless carefully ordered or scoped. – Consider using unique identifiers or separate functions/mocks per test case scenario to avoid overlap issues. ### Debugging Tips – **Print Statements**: Temporarily add print statements inside lambdas or use logging within mocks to verify which paths are being executed. – **Isolation**: Test each patch individually first before combining them again. – **Simplify Tests**: Break down complex tests into smaller ones focusing on specific behaviors (e.g., only one role check). ### Group Membership Influence Flask-Security-Too uses group membership checks during authentication flows typically tied via LDAP queries translated into database queries within Flask apps contextually through user roles management systems (like SQLAlchemy models). Ensure that any custom logic aligns well with Flask-Security-Too expectations regarding user-role mappings derived from LDAP groups within those queries/models contextually set up by security configurations (`app.security`. If issues persist after these checks/debugging steps consider revisiting documentation specific sections about LDAP integration nuances within Flask-Security-Too environment setups! Hope this helps clear things up! 😊 If further issues arise feel free ask more questions! <>: Alright, got it about the order being important due to stacking from bottom-up – makes sense now why my earlier tests were tripping over themselves because they weren’t isolated enough from each other when stacked together due probably overlapping patches inadvertently affecting subsequent ones differently than intended because they weren’t scoped properly maybe? So taking ur advice i’ll restructure them separating concerns more clearly possibly by defining separate mock objects per scenario before applying them so they don’t interfere right? And yeah will double-check those string comparisons too make sure they’re spot-on reflecting actual role names without any typos or mismatches cause obviously that would throw off everything wouldn’t it? <> Exactly! Restructuring by defining separate mock objects per scenario will help isolate them effectively and prevent unintended interference between tests due to overlapping patches. ### Steps Forward – **Separate Mocks**: Create distinct mock objects/functions for each scenario before applying them with decorators. – **Clear Naming**: Use descriptive names for mocks/scenarios so it’s clear what each is testing. – **Verify Comparisons**: Double-check string comparisons against actual role names used within your application setup—typos can definitely lead astray! Implementing these changes should help clarify test outcomes by ensuring each scenario operates independently without cross-contamination from others. Feel free reach out again if more questions pop up along way! 😊 Good luck refining those tests! <>: Okay then let’s tackle those string comparisons first since they’re fundamental right? If we assume my actual role names are something like [‘ROLE_SUPERADMIN’, ‘ROLE_GROUP_MEMBER’], then my lambdas should probably look something like this instead? python email_filter_patch_superadmin.side_effect=lambda role_name,*args,**kwargs:’ROLE_SUPERADMIN’%role_name==’ROLE_SUPERADMIN’ email_filter_patch_group.side_effect=lambda role_name,*args,**kwargs:’ROLE_GROUP_MEMBER’%role_name==’ROLE_GROUP_MEMBER’ email_filter_patch_not_a_member.side_effect=lambda role_name,*args,**kwargs:not (‘ROLE_SUPERADMIN’%role_name==’ROLE_SUPERADMIN’)and not (‘ROLE_GROUP_MEMBER’%role_name==’ROLE_GROUP_MEMBER’) … rest remains unchanged … And about creating separate mocks per scenario – would it be better practice then just define them outside my test function scope? Something along these lines perhaps? python class TestLDAPAuthentication(unittest.TestCase): def setUp(self): super(TestLDAPAuthentication,self).setUp() def create_mock_for_superadmin(): return unittest.mock.MagicMock(side_effect=lambda role:*([‘ROLE_SUPERADMIN’].__contains__(role))) def create_mock_for_group(): return unittest.mock.MagicMock(side_effect=lambda role:*([‘ROLE_GROUP_MEMBER’].__contains__(role))) … additional setup methods … def test_authenticate_user_with_groups_membership(self): email_filter_patch_superadmin=create_mock_for_superadmin() email_filter_patch_group=create_mock_for_group() … apply patches … … rest remains unchanged … Would setting up mocks this way make my tests cleaner? <> Yes, refining those string comparisons is crucial since accurate checks ensure correct behavior simulation during testing. ### Updated String Comparisons Your updated lambdas look good but simplify them further without unnecessary formatting operations: python email_filter_patch_superadmin.side_effect=lambda role_name,*args,**kwargs:’ROLE_SUPERADMIN’==role_name email_filter_patch_group.side_effect=lambda role_name,*args,**kwargs:’ROLE_GROUP_MEMBER’==role_name email_filter_patch_not_a_member.side_effect=lambda role:*not (‘ROLE_SUPERADMIN’)==*or not (‘ROLE_GROUP_MEMBER’)==* ### Separate Mock Setup Defining mocks outside the test function scope indeed promotes cleaner organization: #### Example Setup Using Class Methods python class TestLDAPAuthentication(unittest.TestCase): def setUp(self): super(TestLDAPAuthentication,self).setUp() def create_mock_for_superadmin(): return unittest.mock.MagicMock(side_effect=lambda role:*([‘ROLE_SUPERADMIN’].__contains__(role))) def create_mock_for_group(): return unittest.mock.MagicMock(side_effect=lambda role:*([‘ROLE_GROUP_MEMBER’].__contains__(role))) def test_authenticate_user_with_groups_membership(self): email_filter_patch_superadmin=create_mock_for_superadmin() email_filter_patch_group=create_mock_for_group() @patch(‘flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role’, email_filter_patch_superadmin) @patch(‘flask_securitytoo.models.db.session.query(User).filter_by.email_address.return_value.one.return_value.has_role’, email_filter_patch_group) @patch(‘flask_securitytoo.views.login.user_lookup’, lambda *args,**kwargs:return mock.Mock(has_roles=[‘group_member’])) def inner_test(): # Your test logic here inner_test() … additional tests … This approach keeps setup clean while maintaining flexibility across different scenarios—each mock tailored specifically without interference from others! Feel free reach out again anytime more questions arise—happy testing! 🚀😊🧪💻✨